{"id":30,"date":"2022-06-02T18:31:27","date_gmt":"2022-06-02T22:31:27","guid":{"rendered":"https:\/\/carleton.ca\/rncsl\/?page_id=30"},"modified":"2025-10-08T08:48:57","modified_gmt":"2025-10-08T12:48:57","slug":"research","status":"publish","type":"page","link":"https:\/\/carleton.ca\/rncsl\/research\/","title":{"rendered":"RESEARCH"},"content":{"rendered":"

Check out our <\/strong> Publications<\/strong><\/a><\/p>\n

Control Design of Unmanned Aerial and Ground Vehicles<\/a><\/dt>


\nDesigning controllers for Unmanned Aerial vehicles (UAVs) including quadrotors and Autonomous Ground Vehicles (AGVs), are challenging regarding control design, model representation, and stability. Various control approaches have been employed to address the control system limitations of UAVs\/AGVs and enhance the stability, guidance, and navigation.<\/p>\n

Have a look at our contributions<\/b>
\n[Paper6<\/a><\/b>] , [Paper5<\/a><\/b>] , [Paper4<\/a><\/b>] , [Paper3<\/a><\/b>] , [Paper2<\/a><\/b>] , [Paper1<\/a><\/b>]
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Reinforcement Learning and Predictive Control<\/a><\/dt>


\nThe robotics community has extensively embraced Reinforcement Learning (RL) algorithms for controlling complex single-robot and multi-robot systems. Model predictive control (MPC), also known as receding horizon control, is an advanced control approach that is important for industrial process control and gained popularity because it considers control input and state constraints.<\/p>\n

Have a look at our contributions<\/b>
\n[Paper3<\/a><\/b>] , [Paper2<\/a><\/b>] , [Paper1<\/a><\/b>]
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Observer-based Controllers for Unmanned Vehicles<\/a><\/dt>


\nIn the field of autonomous navigation, comprehensive autonomous modules able to accurately estimate Unmanned Aerial Vehicle (UAV) motion components and to provide control signals to successfully track the vehicle along the desired trajectory are in great demand. When using a Vision-Aided Inertial Navigation System (VA-INS) composed of a low-cost Inertial Measurement Unit (IMU) and a vision unit (monocular or stereo camera), the UAV motion components that require estimation will include orientation (attitude), gyro bias (if angular velocity is unavailable), position, and linear velocity. Given the fact that vehicle\u2019s attitude, position, and linear velocity are generally unknown, they can be reconstructed utilizing sensor measurements. In our Lab, we develop observer-based controllers for Unmanned Aerial Vehicles (UAVs) and Autonomous Ground Vehicles (AGVs).<\/p>\n

Visit our research Talks\/Videos at international events<\/b>
\n Hashim \u2013 Observer-based Controller for Attitude – Romania<\/a><\/p>\n

Have a look at our contributions<\/b>
\n[Paper3<\/a><\/b>] , [Paper2<\/a><\/b>] , [Paper1<\/a><\/b>]
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Vision-based Aided Navigation for GPS-denied Regions<\/a><\/dt>


\nRobust and accurate navigation solutions for autonomous vehicles are essential. Indoor and outdoor applications, such as household cleaning devices, pipelines, terrain mapping, reef monitoring, exemplify situations when GPS might be unreliable and only low-cost measurement units (e.g., inertial measurement unit (IMU)) might be available. In such a case GPS-independent navigation solutions are indispensable. A typical low-cost IMU module is composed of an accelerometer and a gyroscope which provide measurements of rigid-body\u2019s acceleration and angular velocity, respectively. In the absence of GPS, a cost-effective autonomous vehicle requires navigation solutions that rely on low-cost IMU and feature measurements collected by a vision unit. Hence, autonomous navigation in space requires estimation of orientation (known as attitude), position, and linear velocity. An inertial vision unit composed of a stereo vision unit and an IMU can be employed to extract rigid-body\u2019s pose – a combination of attitude and position. In our Lab, we develop stochastic and deterministic nonlinear estimation techniques for vehicles navigating with six degrees of freedom (6 DoF) applicable for Unmanned Aerial Vehicles (UAVs) and Autonomous Ground Vehicles (AGVs).<\/p>\n